comparison DL/two-step DL/SMALL_two_step_DL.m @ 153:af307f247ac7 ivand_dev

Example scripts for Two Step Dictionary Learning - Image Denoising experiments.

     decorrelate = 0;
 end
 
 % show dictonary every specified number of iterations
 
-if (isfield(DL.param,'show_dict'))
+if isfield(DL.param,'show_dict')
     show_dictionary=1;
     show_iter=DL.param.show_dict;
 else
     show_dictionary=0;
     show_iter=0;

 % version of software we store the signal that needs to be represented
 % (for example the whole image)
 
 tmpTraining = Problem.b1;
 Problem.b1 = sig;
-Problem = rmfield(Problem, 'reconstruct');
+if isfield(Problem,'reconstruct')
+    Problem = rmfield(Problem, 'reconstruct');
+end
 solver.profile = 0;
 
 % main loop %
 
 for i = 1:iternum
+    Problem.A = dico;
     solver = SMALL_solve(Problem, solver);
     [dico, solver.solution] = dico_update(dico, sig, solver.solution, ...
         typeUpdate, flow, learningRate);
     if (decorrelate)
         dico = dico_decorr(dico, mu, solver.solution);
     end
-    Problem.A = dico;
+    
    if ((show_dictionary)&&(mod(i,show_iter)==0))
        dictimg = SMALL_showdict(dico,[8 8],...
             round(sqrt(size(dico,2))),round(sqrt(size(dico,2))),'lines','highcontrast');  
        figure(2); imagesc(dictimg);colormap(gray);axis off; axis image;
        pause(0.02);

 
 Problem.b1 = tmpTraining;
 DL.D = dico;
 
 end
+
+function Y = colnorms_squared(X)
+
+% compute in blocks to conserve memory
+Y = zeros(1,size(X,2));
+blocksize = 2000;
+for i = 1:blocksize:size(X,2)
+  blockids = i : min(i+blocksize-1,size(X,2));
+  Y(blockids) = sum(X(:,blockids).^2);
+end
+
+end